Comments (4)
As a matter of fact compared to original correlation implementation, you can !
Consider the parameters in FlowNetC : k=1, patch=21, stride=1, padding=0, dilation_patch=2
The parameters can acutally accept a tuple of H and W values. So an equivalent correlation module for DispNet would be
k=1, patch=(1,21), paddinf=0, dilation_patch=2
My guess is that the Lab that created the correlation module (LMB Freiburg) uses DispNetS in all kind of big projects and never felt the need to make 1D Correlation Module, so it may not be as interesting as it is for FlowNetC, but you can try !
Clément
from pytorch-correlation-extension.
ok, thanks very much, I think you may consider this case in your pytorch implementation.
you mean I can try the parameter patch=(1, 21) in your implementation?
from pytorch-correlation-extension.
Yes. You can try with a toy problem, but normally you should end up with a B x 1 x 21 x H x W
tensor, which you can just squeeze into a B x 21 x H x W
tensor and feed it to the rest of your network.
from pytorch-correlation-extension.
Ok, I'll try it, thanks!
from pytorch-correlation-extension.
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